ArticleBioMed research international2013
DeGNServer: deciphering genome-scale gene networks through high performance reverse engineering analysis.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 23 citations in OpenAlex.
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- Bioinformatics analysis of the proteins interacting with LASP-1 and their association with HBV-related hepatocellular carcinoma.Scientific reports · 2017Article
- Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.Scientific reports · 2016Article
- LegumeIP 2.0--a platform for the study of gene function and genome evolution in legumes.Nucleic acids research · 2016Article
- Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.Frontiers in microbiology · 2016Article
- Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.Frontiers in plant science · 2016Article
- DTW-MIC Coexpression Networks from Time-Course Data.PloS one · 2016Article
- Methanol regulated yeast promoters: production vehicles and toolbox for synthetic biology.Microbial cell factories · 2015Article
- Functional characterization of drought-responsive modules and genes in Oryza sativa: a network-based approach.Frontiers in genetics · 2015Article
- The personal touch: strategies toward personalized vaccines and predicting immune responses to them.Expert review of vaccines · 2014Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.